Computer systems and their related technologies that are currently available enable the acquisition and storing of nearly endless amounts of data and information regarding a seemingly endless variety of subject matter. Those systems and technologies also enable the acquired data and information to be categorized and classified according to an almost limitless selection of attributes.
Accordingly, numerous methods have been implemented to utilize the acquired data and information for purposeful interpretation. To provide purposeful interpretation of data and information acquired, numerous methods have been implemented in an attempt to provide an optimal action as a response to particular sets of criterion relative to a customer. Referring to the various methods that are commonly utilized, each has focused primarily on one particular approach to interpretation and utilization of the outcome to provide the optimal action relative to a particular subject. It should be appreciated that each method is not without certain drawbacks.
In a first example, one method calculates the estimated objective function for nearly any of the possible actions and simply selects the one with maximum value. In this example, each subject is a customer who has accessed a company's Web site for a particular reason. In this same example, an action is something promoted by a company, such as, for example, something for sale. While this approach provides complete flexibility for the estimation, it uses a “greedy algorithm” for finding the optimal allocation strategy. A greedy algorithm is in reference to the manner in which the optimal action is derived. It bases the decision solely on a single factor, such as e.g., profit. For example, assume a company is selling computers, and each of the computers comes with different configurations and prices dependent upon those configurations. In this instance, the optimal action, the selling of the computer, would be based on the computer with the highest selling price and, in one example, the largest amount of profit. By essentially ignoring any business side constraints such as, e.g., budget, inventory levels, projected sales, shipping costs, and the optimal action can produce results that are far from being optimal.
In a second example, a customer is placed into several groups using, for instance, a clustering algorithm or supervised learning. The customers within a particular grouping are then treated as homogenous in their response to nearly any action. This enables solving the optimization problem with a finite number of groupings. For each new customer, the appropriate groups are determined, and the optimal action for those groups are applied to that new customer. While this approach provides for a wider range than that of the optimal action based on maximum value, it restricts the estimation to be only of a segmentation type. A segmentation type of estimation may not provide an optimal action for a given application. It is well known in the art that estimators derived and/or obtained from a generalized linear regression or from a neural network are not analogous to the segmentation type.
Additionally, to ensure that the sample size is sufficient to estimate the outcome within each of the groups in the segmentation type of estimation, the total number of groups must be kept rather small. However, to find a segmentation where the customers of each group are homogenous to almost all the optimum action possibilities would be very unlikely.
In another example, numerous complex procedures are utilized to attempt to estimate the joint distribution of the estimated outcome space. This approach suffers from the inherent complexity of performing the complex procedures, but also from the fact that it is quite difficult to provide estimation based upon a high dimensional distribution.
Thus a need exists for a method of estimation and optimization of an action on subjects that is based on an objective of an outcome which incorporates attributes relative to each subject. A further need exists for a method that considers each subject as a separate segment. An additional need exists for a method which includes business side constraints. Additionally, a need exists for a method that reduces the computational complexity normally associated with previous methods. Further, a need exists for a method that incorporates full flexibility into the estimation function. Another need exists for a method that can separate the estimation portion from the optimization portion, so as to strike a balance between optimality and tractability.
The present invention provides a unique, novel solution to those and other problems.